A Feature-Enriched Method for User Intent Classification by Leveraging Semantic Tag Expansion

Xie, Wenxiu; Gao, Dongfa; Ding, Ruoyao; Hao, Tianyong

doi:10.1007/978-3-319-99501-4_19

Cited by 7 publications

(7 citation statements)

References 18 publications

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“…Previous works have shown that adding POS tags as features improves IC (Zhang et al, 2016;Xie et al, 2018) as well SF performances (Firdaus et al, 2018) in many-shot settings. In this work we look into incorporating syntactic features in our metalearning pipeline.…”

Section: D1 Feature-based Additionmentioning

confidence: 97%

Semi-Supervised Few-Shot Intent Classification and Slot Filling

Basu,

Chong,

Sharaf

et al. 2021

Preprint

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Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data to train deep learning models for such systems is not scalable. This problem can be addressed by learning from few examples using fast supervised meta-learning techniques such as prototypical networks. In this work, we systematically investigate how contrastive learning and unsupervised data augmentation methods can benefit these existing supervised meta-learning pipelines for jointly modelled IC/SF tasks. Through extensive experiments across standard IC/SF benchmarks (SNIPS and ATIS), we show that our proposed semi-supervised approaches outperform standard supervised meta-learning methods: contrastive losses in conjunction with prototypical networks consistently outperform the existing state-of-the-art for both IC and SF tasks, while data augmentation strategies primarily improve few-shot IC by a significant margin.

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Section: D1 Feature-based Additionmentioning

confidence: 97%

Semi-Supervised Few-Shot Intent Classification and Slot Filling

Basu,

Chong,

Sharaf

et al. 2021

Preprint

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“…Adversarial training method for the multi-task and multi-lingual joint modelling [Mohasseb et al 2018] Grammar feature exploration Grammar-based framework with 3 main features [Xie et al 2018] Short text; Semantic feature expansion Semantic Tag-empowered combined features [Qiu et al 2018] Potential consciousness information mining A similarity calculation method based on LSTM and a traditional machine learning method based on multi-feature extraction OOD utterances Multi-task learning [Cohan et al 2019] Utilisation of naturally labelled data Multitask learning based on joint loss [Shridhar et al 2019] OOV issue; Small/lack of labelled training data Subword semantic hashing ] Learning of deep semantic information Hybrid CNN and bidirectional GRU neural network with pretrained embeddings (Char-CNN-BGRU) [Lin and Xu 2019] Emerging intents detection Maximise inter-class variance and minimise intra-class variance to get the discriminative feature [Ren and Xue 2020] Similar utterance with different intent Triples of samples used for training [Yilmaz and Toraman 2020] OOD utterances KL divergence vector for classification [Costello et al 2018] developed a novel multi-layer ensembling approach that ensembles both different model initialisation and different model architectures to determine how multi-layer ensembling improves performance on multilingual intent classification. They constructed a CNN with character-level embedding and a bidirectional CNN with attention mechanism.…”

Section: Papermentioning

confidence: 99%

“…[ Xie et al 2018] proposed a model called Semantic Tagempowered User Intent Classification (ST-UIC), based on a constructed semantic tag repository. This model uses a combination of four kinds of features including characters, non-key-noun part-ofspeech tags, target words, and semantic tags.…”

Section: 3mentioning

confidence: 99%

A survey of joint intent detection and slot-filling models in natural language understanding

Weld¹,

Huang²,

Long³

et al. 2021

Preprint

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Intent classification and slot filling are two critical tasks for natural language understanding. Traditionally the two tasks have been deemed to proceed independently. However, more recently, joint models for intent classification and slot filling have achieved state-of-the-art performance, and have proved that there exists a strong relationship between the two tasks. This article is a compilation of past work in natural language understanding, especially joint intent classification and slot filling. We observe three milestones in this research so far: Intent detection to identify the speaker's intention, slot filling to label each word token in the speech/text, and finally, joint intent classification and slot filling tasks. In this article, we describe trends, approaches, issues, data sets, evaluation metrics in intent classification and slot filling. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. To interpret the state-of-the-art trends, we provide multiple tables that describe and summarise past research along different dimensions, including the types of features, base approaches, and dataset domain used.

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“…Khalil et al [31] explored the intention classification based on the multilingual transfer ability of English and French. Xie et al [32] used the multiple semantic features to study Chinese user intention classification based on ECDT [33] dataset. Attention-based BiGRU-CNN [16] model was proposed for Chinese question classification based on the Fudan University Chinese question dataset.…”

Section: Complexitymentioning

confidence: 99%

“…TP represents the number of samples predicted correctly, FP represents the number of samples that are incorrectly predicted, FN is the number of samples that are incorrectly predicted of other categories, and TN is the number of samples that are correctly predicted of other categories. [32]: the method used a traditional logistic regression with four feature expansions.…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Hybrid Neural Network BERT-Cap Based on Pre-Trained Language Model and Capsule Network for User Intent Classification

Liu

Wong

et al. 2020

Complexity

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User intent classification is a vital component of a question-answering system or a task-based dialogue system. In order to understand the goals of users’ questions or discourses, the system categorizes user text into a set of pre-defined user intent categories. User questions or discourses are usually short in length and lack sufficient context; thus, it is difficult to extract deep semantic information from these types of text and the accuracy of user intent classification may be affected. To better identify user intents, this paper proposes a BERT-Cap hybrid neural network model with focal loss for user intent classification to capture user intents in dialogue. The model uses multiple transformer encoder blocks to encode user utterances and initializes encoder parameters with a pre-trained BERT. Then, it extracts essential features using a capsule network with dynamic routing after utterances encoding. Experiment results on four publicly available datasets show that our model BERT-Cap achieves a F1 score of 0.967 and an accuracy of 0.967, outperforming a number of baseline methods, indicating its effectiveness in user intent classification.

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A Feature-Enriched Method for User Intent Classification by Leveraging Semantic Tag Expansion

Cited by 7 publications

References 18 publications

Semi-Supervised Few-Shot Intent Classification and Slot Filling

Semi-Supervised Few-Shot Intent Classification and Slot Filling

A survey of joint intent detection and slot-filling models in natural language understanding

A Hybrid Neural Network BERT-Cap Based on Pre-Trained Language Model and Capsule Network for User Intent Classification

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